Assessing LEED versus Non-LEED Energy Consumption for a University Campus in North America: A Preliminary Study

El objetivo es visualizar y simular el consumo de energía en América del Norte, mediante pirámides de cuatro caras, en donde el polígono de la base está conformado por cuatro puntos del consumo no renovable, y la cúspide, representada por el ápice o punto superior, corresponde al consumo o alternativas renovables. Se inicia con los antecedentes del consumo de energía en el mundo, siguiendo con la revisión de la literatura acerca de propuestas relacionadas a la demanda de petróleo y derivados. Posteriormente se presenta la metodología y simulación encontrando en los resultados que, reduciendo consumo de productos de refinamiento medio y combustibles, se provocaría mayor altura en la cúspide de las pirámides, interpretada como el aumento de las energías renovables.

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Mobile Application for Electric Power Monitoring on Energy Consumptions at a Campus University

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v11.i2.pp637-644 ◽

2018 ◽

Vol 11 (2) ◽

pp. 637

Author(s):

Murizah Kassim ◽

Maisarah Abdul Rahman ◽

Cik Ku Haroswati Che Ku Yahya ◽

Azlina Idris

Keyword(s):

Energy Consumption ◽

Electric Power ◽

Mobile Applications ◽

Working Hours ◽

Mobile App ◽

Smart Meter ◽

University Campus ◽

Smart Meters ◽

Power Monitoring ◽

Energy Consumptions

This paper presents a research on electric power monitoring prototype mobile applications development on energy consumptions in a university campus. Electric power energy consumptions always are the issue of monitoring usage especially in a broad environment. University campus faces high used of electric power, thus crucial analysis on cause of the usage is needed. This research aims to analyses electric power usage in a university campus where implemented of few smart meters is installed to monitor five main buildings in a campus university. A Monitoring system is established in collecting electric power usage from the smart meters. Data from the smart meter then is analyzed based on energy consume on 5 buildings. Results presents graph on the power energy consume and presented on mobile applications using Live Code coding. The methodology involved the setup of the smart meters, monitoring and data collected from main smart meters, analyzed electrical consumptions for 5 buildings and mobile system development to monitor. A Live Code mobile app is designed then data collected from smart meter using ION software is published in graphs. Results presents the energy consumed for 5 building during day and night. Details on maximum and minimum energy consumption presented that show load of energy used in the campus. Result present Tower 1 saved most eenergy at night which is 65% compared to block 3 which is 8% saved energy although block 3 presents the lowest energy consumption in the working hours and non-working hours. This project is significant that can help campus facility to monitor electric power used thus able to control possible results in future implementations.

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Wireless Home Energy Meter Manager using Android: A Preliminary study

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v18n3-2.197 ◽

2019 ◽

Vol 18 (3-2) ◽

pp. 32-36

Author(s):

Sh. Nurul Hidayah Wan Julihi ◽

Ili Najaa Aimi Mohd Nordin ◽

Muhammad Rusydi Muhammad Razif ◽

Amar Faiz Zainal Abidin

Keyword(s):

Energy Consumption ◽

Personal Information ◽

Current Sensor ◽

Smart Meter ◽

Smart Meters ◽

Long Distance ◽

Energy Meter ◽

Utility Companies ◽

Preliminary Study ◽

To Receive

Manual home energy meter reading and billing had caused inconvenience to the utility companies due to lack of manpower to read the energy meter at each household especially in the remote area, explains the increasing number of smart meter reader in the current market. Most of the smart meters in the market do not offer safety of privacy of consumers’ personal information since the data of electricity usage is being transferred digitally to the utility companies for more accurate bills calculation. Plus, the smart meter system is also a bit pricey to be installed in the rural area. Therefore, a private system that able to read energy consumption from a DC load and calculate its bill according to the tariff is proposed. Value of current is being obtained by using ACS712 current sensor. Hall circuit in the current sensor will converts magnetic field into a proportional voltage. The proposed system allows energy meter monitoring from an Android-based smartphone by displaying the real-time energy consumption and bill on Blynk application. An interface of Blynk is developed and connected to WiFi module, ESP8266 for visualizing the energy consumption of the DC load. In conclusion, the Energy Meter transmitter part able to read, calculate and transmit value of energy consumption and current bills to the Blynk application and Blynk application able to receive and show all the data transmitted at the present time. This system will be further improved for long-distance monitoring of electrical appliances used at home.

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An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study

Journal of Energy Resources Technology ◽

10.1115/1.4023741 ◽

2013 ◽

Vol 135 (3) ◽

Cited By ~ 11

Author(s):

David Palchak ◽

Siddharth Suryanarayanan ◽

Daniel Zimmerle

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Consumption ◽

Time Of Day ◽

University Campus ◽

Electrical Load ◽

Ann Model ◽

Short Term ◽

Management Options ◽

Artificial Neural

This paper presents an artificial neural network (ANN) for forecasting the short-term electrical load of a university campus using real historical data from Colorado State University. A spatio-temporal ANN model with multiple weather variables as well as time identifiers, such as day of week and time of day, are used as inputs to the network presented. The choice of the number of hidden neurons in the network is made using statistical information and taking into account the point of diminishing returns. The performance of this ANN is quantified using three error metrics: the mean average percent error; the error in the ability to predict the occurrence of the daily peak hour; and the difference in electrical energy consumption between the predicted and the actual values in a 24-h period. These error measures provide a good indication of the constraints and applicability of these predictions. In the presence of some enabling technologies such as energy storage, rescheduling of noncritical loads, and availability of time of use (ToU) pricing, the possible demand-side management options that could stem from an accurate prediction of energy consumption of a campus include the identification of anomalous events as well the management of usage.

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Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network

Energies ◽

10.3390/en12163135 ◽

2019 ◽

Vol 12 (16) ◽

pp. 3135 ◽

Cited By ~ 5

Author(s):

Carolina Del-Valle-Soto ◽

Leonardo J. Valdivia ◽

Ramiro Velázquez ◽

Luis Rizo-Dominguez ◽

Juan-Carlos López-Pimentel

Keyword(s):

Energy Consumption ◽

Sensor Networks ◽

Performance Metrics ◽

Smart Cities ◽

Performance Comparison ◽

Wireless Sensor ◽

University Campus ◽

Multiple Sensors ◽

Energy Consumption Analysis ◽

Wireless Environment

Presently, the Internet of Things (IoT) concept involves a scattered collection of different multipurpose sensor networks that capture information, which is further processed and used in applications such as smart cities. These networks can send large amounts of information in a fairly efficient but insecure wireless environment. Energy consumption is a key aspect of sensor networks since most of the time, they are battery powered and placed in not easily accessible locations. Therefore, and regardless of the final application, wireless sensor networks require a careful energy consumption analysis that allows selection of the best operating protocol and energy optimization scheme. In this paper, a set of performance metrics is defined to objectively compare different kinds of protocols. Four of the most popular IoT protocols are selected: Zigbee, LoRa, Bluethooth, and WiFi. To test and compare their performance, multiple sensors are placed at different points of a university campus to create a network that can accurately simulate a smart city. Finally, the network is analyzed in detail using two different schemes: collaborative and cooperative.

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